einsum_benchmark

v0.1.7

Wrapper package for the einsum benchmark dataset and code to create new instances of the various types used in the benchmark For more information about how to use this package see README

Latest version published 5 months ago

License: MIT

PyPI

GitHub

Package Health Score

57 / 100

security
No known security issues
popularity
Limited
maintenance
Sustainable
community
Limited

Popularity

Limited

GitHub Stars: 1
Forks: 0
Contributors: 1

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package einsum_benchmark, we found that it has been starred 1 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Jun 18, 2024
Direct Vulnerabilities: 0
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0
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0
M
0
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Indirect Vulnerabilities: 0
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0
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0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 0
Open PR: 0
Last Release: 5 months ago
Last Commit: 6 months ago

Further analysis of the maintenance status of einsum_benchmark based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that einsum_benchmark demonstrates a positive version release cadence with at least one new version released in the past 12 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Limited

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 1
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: >=3.10

Age: 6 months
Latest Release: 5 months ago
Dependencies: 0 Direct
Versions: 7
Maintainers: 1
Wheels: OS Independent

Readme

Einsum Benchmark Data

Benchmark data repository accompanying the paper: "Einsum Benchmark: Enabling the Development of Next-Generation Tensor Execution Engines". It includes a package to easily work with the benmark data and all methods we used to convert and create our instances so you can create new ones yourself.

Installation

Install our base package, you will need at least python 3.10:

pip install einsum_benchmark

Basic Usage

Now you can load and run an instance like this:

import opt_einsum as oe
import einsum_benchmark

instance = einsum_benchmark.instances["qc_circuit_n49_m14_s9_e6_pEFGH_simplified"]

opt_size_path_meta = instance.paths.opt_size
print("Size optimized path")
print("log10[FLOPS]:", round(opt_size_path_meta.flops, 2))
print("log2[SIZE]:", round(opt_size_path_meta.size, 2))
result = oe.contract(
    instance.format_string, *instance.tensors, optimize=opt_size_path_meta.path
)
print("sum[OUTPUT]:", result.sum(), instance.result_sum)

For more information please read our docs.

The code for our experiments is here: https://github.com/ti2-group/einsum_benchmark_experiments

Acknowledgements

The broader data collection process included contributions from individuals whose data was transformed. We duly acknowledge the following for making their data publicly available:

Fichte, Johannes; Hecher, Markus; Florim Hamiti: Model Counting Competition 2020
Fichte, Johannes; Hecher, Markus: Model Counting Competition 2021 2022 2023
Fichte, Johannes; Hecher, Markus; Woltran, Stefan; Zisser, Markus: A Benchmark Collection of #SAT Instances and Tree Decompositions
Meel, Kuldeep S.: Model Counting and Uniform Sampling Instances
Automated Reasoning Group at the University of California, Irvine: UAI Competitions
Martinis, John M. et al.: Quantum supremacy using a programmable superconducting processor Dataset. Dryad.

Moreover, we thank the following authors of open source software used to generated instances:

Gray, Johnnie: quimb, cotengra
Soos, Mate, Meel, Kuldeep S: Arjun
Stoian, Mihail: Netzwerk
Liu, Jinguo; Lua, Xiuzhe; Wang, Lei: Yao.jl
Liu, Jinguo: YaoToEinsum.jl

FAQs

What is einsum_benchmark?

Wrapper package for the einsum benchmark dataset and code to create new instances of the various types used in the benc... Visit Snyk Advisor to see a full health score report for einsum_benchmark, including popularity, security, maintenance & community analysis.

Is einsum_benchmark popular?

We’re still processing downloads data for the python package einsum_benchmark. Visit the popularity section on Snyk Advisor to see the full popularity analysis.

Is einsum_benchmark well maintained?

We found indications that einsum_benchmark maintenance is sustainable demonstrating some project activity. We saw a total of 1 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is einsum_benchmark safe to use?

The python package einsum_benchmark was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 25 November-2024, at 17:28 (UTC).

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